Concurrent Lung Pathology in Dogs with Chronic Liver Disease
Bibliographic record
Abstract
Remote lung injury is a frequent sequela of chronic liver disease, especially liver cirrhosis, in people. Anecdotal evidence of concurrent lung pathology in dogs with chronic liver disease warrants further investigation in this species. The objective was to perform a retrospective analysis of dogs diagnosed with cirrhotic and non‐cirrhotic chronic liver disease and determine if there is radiographic and/or histopathologic evidence of concurrent pulmonary pathology. Dogs were diagnosed with chronic liver disease based on history, clinical signs, clinicopathological abnormalities, diagnostic imaging, biopsy, and/or necropsy. Only dogs where liver disease was confirmed with diagnostic imaging, liver biopsy, and/or necropsy were included in the study (n=28). Evidence of concurrent lung pathology was based on radiographic and/or histopathologic findings. 23/28 dogs (82%) (95% CI: 63‐94%) had evidence of lung pathology. 18/28 dogs (64%) (95% CI: 44%‐81%) had an interstitial pattern on thoracic radiographs. A specific diagnosis of liver cirrhosis was not related to concurrent lung pathology (P=0.53) or an interstitial lung pattern (P=0.42). In conclusion, lung pathology was quite common (>80%) in dogs with liver disease. However, there was no clear relationship between liver cirrhosis and concurrent lung pathology or interstitial lung pattern in our group of dogs. Prospective research is needed to further evaluate dogs as an animal model for study of remote lung injury in chronic liver disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".